Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations7228
Missing cells0
Missing cells (%)0.0%
Duplicate rows152
Duplicate rows (%)2.1%
Total size in memory2.5 MiB
Average record size in memory361.0 B

Variable types

Numeric9
Categorical6
Text1

Alerts

Negotiation Type has constant value "rent" Constant
Property Type has constant value "apartment" Constant
Dataset has 152 (2.1%) duplicate rowsDuplicates
Condo is highly overall correlated with Parking and 2 other fieldsHigh correlation
Parking is highly overall correlated with Condo and 5 other fieldsHigh correlation
Price is highly overall correlated with Condo and 3 other fieldsHigh correlation
Rooms is highly overall correlated with Parking and 2 other fieldsHigh correlation
Size is highly overall correlated with Condo and 5 other fieldsHigh correlation
Suites is highly overall correlated with Parking and 2 other fieldsHigh correlation
Toilets is highly overall correlated with Parking and 4 other fieldsHigh correlation
New is highly imbalanced (99.0%) Imbalance
Condo has 640 (8.9%) zeros Zeros
Suites has 1715 (23.7%) zeros Zeros
Parking has 294 (4.1%) zeros Zeros
Latitude has 483 (6.7%) zeros Zeros
Longitude has 483 (6.7%) zeros Zeros

Reproduction

Analysis started2025-05-03 14:28:18.717303
Analysis finished2025-05-03 14:28:51.784687
Duration33.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3077.6691
Minimum480
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:52.283842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile950
Q11350
median2000
Q33300
95-th percentile9000
Maximum50000
Range49520
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation3522.8303
Coefficient of variation (CV)1.1446423
Kurtosis37.090652
Mean3077.6691
Median Absolute Deviation (MAD)800
Skewness4.8741261
Sum22245392
Variance12410333
MonotonicityNot monotonic
2025-05-03T11:28:52.818040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 367
 
5.1%
1200 362
 
5.0%
2500 299
 
4.1%
1300 276
 
3.8%
2000 276
 
3.8%
1600 271
 
3.7%
1400 243
 
3.4%
1100 238
 
3.3%
1800 237
 
3.3%
1000 232
 
3.2%
Other values (463) 4427
61.2%
ValueCountFrequency (%)
480 1
 
< 0.1%
500 5
 
0.1%
550 1
 
< 0.1%
600 7
0.1%
610 1
 
< 0.1%
628 1
 
< 0.1%
630 2
 
< 0.1%
650 13
0.2%
660 3
 
< 0.1%
670 1
 
< 0.1%
ValueCountFrequency (%)
50000 3
< 0.1%
45000 1
 
< 0.1%
40000 3
< 0.1%
38000 1
 
< 0.1%
36000 2
 
< 0.1%
35000 4
0.1%
30000 5
0.1%
29000 1
 
< 0.1%
27000 3
< 0.1%
26000 2
 
< 0.1%

Condo
Real number (ℝ)

High correlation  Zeros 

Distinct1192
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825.1948
Minimum0
Maximum9500
Zeros640
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:53.310191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1395.75
median595
Q3990
95-th percentile2400
Maximum9500
Range9500
Interquartile range (IQR)594.25

Descriptive statistics

Standard deviation835.62194
Coefficient of variation (CV)1.012636
Kurtosis14.561506
Mean825.1948
Median Absolute Deviation (MAD)255
Skewness3.0372388
Sum5964508
Variance698264.02
MonotonicityNot monotonic
2025-05-03T11:28:53.712864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 640
 
8.9%
500 174
 
2.4%
600 152
 
2.1%
400 146
 
2.0%
450 132
 
1.8%
550 120
 
1.7%
700 120
 
1.7%
650 96
 
1.3%
350 86
 
1.2%
1100 86
 
1.2%
Other values (1182) 5476
75.8%
ValueCountFrequency (%)
0 640
8.9%
1 15
 
0.2%
10 3
 
< 0.1%
15 1
 
< 0.1%
25 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
36 1
 
< 0.1%
40 1
 
< 0.1%
50 12
 
0.2%
ValueCountFrequency (%)
9500 1
< 0.1%
8860 1
< 0.1%
8800 1
< 0.1%
8000 1
< 0.1%
7928 1
< 0.1%
7800 1
< 0.1%
7500 2
< 0.1%
6800 2
< 0.1%
6300 2
< 0.1%
6100 1
< 0.1%

Size
Real number (ℝ)

High correlation 

Distinct311
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.486165
Minimum30
Maximum880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:54.679836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q152
median67
Q3100
95-th percentile219
Maximum880
Range850
Interquartile range (IQR)48

Descriptive statistics

Standard deviation63.976416
Coefficient of variation (CV)0.71493081
Kurtosis15.742876
Mean89.486165
Median Absolute Deviation (MAD)18
Skewness3.0542718
Sum646806
Variance4092.9818
MonotonicityNot monotonic
2025-05-03T11:28:55.393231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 424
 
5.9%
60 337
 
4.7%
70 314
 
4.3%
65 247
 
3.4%
55 193
 
2.7%
48 174
 
2.4%
45 166
 
2.3%
56 157
 
2.2%
80 136
 
1.9%
62 128
 
1.8%
Other values (301) 4952
68.5%
ValueCountFrequency (%)
30 44
0.6%
31 16
 
0.2%
32 26
 
0.4%
33 25
 
0.3%
34 20
 
0.3%
35 107
1.5%
36 32
 
0.4%
37 15
 
0.2%
38 44
0.6%
39 20
 
0.3%
ValueCountFrequency (%)
880 1
< 0.1%
852 1
< 0.1%
670 1
< 0.1%
640 1
< 0.1%
627 1
< 0.1%
600 1
< 0.1%
598 1
< 0.1%
574 1
< 0.1%
540 2
< 0.1%
516 1
< 0.1%

Rooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3042335
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:55.705728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82910846
Coefficient of variation (CV)0.35981963
Kurtosis0.79310447
Mean2.3042335
Median Absolute Deviation (MAD)1
Skewness0.34658192
Sum16655
Variance0.68742085
MonotonicityNot monotonic
2025-05-03T11:28:55.921618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 3267
45.2%
3 2314
32.0%
1 1145
 
15.8%
4 485
 
6.7%
5 13
 
0.2%
6 2
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 1145
 
15.8%
2 3267
45.2%
3 2314
32.0%
4 485
 
6.7%
5 13
 
0.2%
6 2
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 13
 
0.2%
4 485
 
6.7%
3 2314
32.0%
2 3267
45.2%
1 1145
 
15.8%

Toilets
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1055617
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:56.243765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.99816189
Coefficient of variation (CV)0.47405967
Kurtosis2.8850304
Mean2.1055617
Median Absolute Deviation (MAD)0
Skewness1.5404123
Sum15219
Variance0.99632716
MonotonicityNot monotonic
2025-05-03T11:28:56.561962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 4267
59.0%
1 1676
 
23.2%
4 499
 
6.9%
3 488
 
6.8%
5 250
 
3.5%
6 39
 
0.5%
7 7
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
1 1676
 
23.2%
2 4267
59.0%
3 488
 
6.8%
4 499
 
6.9%
5 250
 
3.5%
6 39
 
0.5%
7 7
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 7
 
0.1%
6 39
 
0.5%
5 250
 
3.5%
4 499
 
6.9%
3 488
 
6.8%
2 4267
59.0%
1 1676
 
23.2%

Suites
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0244881
Minimum0
Maximum5
Zeros1715
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:56.802281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88424126
Coefficient of variation (CV)0.86310545
Kurtosis2.353241
Mean1.0244881
Median Absolute Deviation (MAD)0
Skewness1.3924997
Sum7405
Variance0.78188261
MonotonicityNot monotonic
2025-05-03T11:28:57.029529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4474
61.9%
0 1715
 
23.7%
3 523
 
7.2%
2 353
 
4.9%
4 159
 
2.2%
5 4
 
0.1%
ValueCountFrequency (%)
0 1715
 
23.7%
1 4474
61.9%
2 353
 
4.9%
3 523
 
7.2%
4 159
 
2.2%
5 4
 
0.1%
ValueCountFrequency (%)
5 4
 
0.1%
4 159
 
2.2%
3 523
 
7.2%
2 353
 
4.9%
1 4474
61.9%
0 1715
 
23.7%

Parking
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.452269
Minimum0
Maximum9
Zeros294
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size112.9 KiB
2025-05-03T11:28:57.381105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88953549
Coefficient of variation (CV)0.61251429
Kurtosis4.9026898
Mean1.452269
Median Absolute Deviation (MAD)0
Skewness1.7853731
Sum10497
Variance0.79127339
MonotonicityNot monotonic
2025-05-03T11:28:57.658287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 4536
62.8%
2 1603
 
22.2%
3 518
 
7.2%
0 294
 
4.1%
4 211
 
2.9%
5 49
 
0.7%
6 11
 
0.2%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 294
 
4.1%
1 4536
62.8%
2 1603
 
22.2%
3 518
 
7.2%
4 211
 
2.9%
5 49
 
0.7%
6 11
 
0.2%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 3
 
< 0.1%
6 11
 
0.2%
5 49
 
0.7%
4 211
 
2.9%
3 518
 
7.2%
2 1603
 
22.2%
1 4536
62.8%
0 294
 
4.1%

Elevator
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size465.9 KiB
0
5061 
1
2167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Length

2025-05-03T11:28:58.062807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:28:58.300352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Most occurring characters

ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5061
70.0%
1 2167
30.0%

Furnished
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size465.9 KiB
0
5978 
1
1250 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Length

2025-05-03T11:28:58.545782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:28:58.801247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5978
82.7%
1 1250
 
17.3%

Swimming Pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size465.9 KiB
0
3701 
1
3527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

Length

2025-05-03T11:28:59.092681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:28:59.312474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

Most occurring characters

ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3701
51.2%
1 3527
48.8%

New
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size465.9 KiB
0
7222 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%

Length

2025-05-03T11:28:59.555841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:28:59.794483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7222
99.9%
1 6
 
0.1%
Distinct94
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size918.0 KiB
2025-05-03T11:29:00.391827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length28
Median length24
Mean length19.355008
Min length12

Characters and Unicode

Total characters139898
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArtur Alvim/São Paulo
2nd rowArtur Alvim/São Paulo
3rd rowArtur Alvim/São Paulo
4th rowArtur Alvim/São Paulo
5th rowArtur Alvim/São Paulo
ValueCountFrequency (%)
paulo 7228
40.2%
vila 1049
 
5.8%
campo 323
 
1.8%
pinheiros/são 286
 
1.6%
jardim 208
 
1.2%
mooca/são 178
 
1.0%
itaim 168
 
0.9%
paulista/são 165
 
0.9%
são 165
 
0.9%
cidade 164
 
0.9%
Other values (107) 8033
44.7%
2025-05-03T11:29:01.439621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 18904
13.5%
a 17379
12.4%
10739
 
7.7%
l 9943
 
7.1%
u 9877
 
7.1%
P 8297
 
5.9%
S 8135
 
5.8%
ã 8041
 
5.7%
/ 7228
 
5.2%
i 5903
 
4.2%
Other values (44) 35452
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 18904
13.5%
a 17379
12.4%
10739
 
7.7%
l 9943
 
7.1%
u 9877
 
7.1%
P 8297
 
5.9%
S 8135
 
5.8%
ã 8041
 
5.7%
/ 7228
 
5.2%
i 5903
 
4.2%
Other values (44) 35452
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 18904
13.5%
a 17379
12.4%
10739
 
7.7%
l 9943
 
7.1%
u 9877
 
7.1%
P 8297
 
5.9%
S 8135
 
5.8%
ã 8041
 
5.7%
/ 7228
 
5.2%
i 5903
 
4.2%
Other values (44) 35452
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 18904
13.5%
a 17379
12.4%
10739
 
7.7%
l 9943
 
7.1%
u 9877
 
7.1%
P 8297
 
5.9%
S 8135
 
5.8%
ã 8041
 
5.7%
/ 7228
 
5.2%
i 5903
 
4.2%
Other values (44) 35452
25.3%

Negotiation Type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size487.0 KiB
rent
7228 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28912
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrent
2nd rowrent
3rd rowrent
4th rowrent
5th rowrent

Common Values

ValueCountFrequency (%)
rent 7228
100.0%

Length

2025-05-03T11:29:01.884087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:29:02.116932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
rent 7228
100.0%

Most occurring characters

ValueCountFrequency (%)
r 7228
25.0%
e 7228
25.0%
n 7228
25.0%
t 7228
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7228
25.0%
e 7228
25.0%
n 7228
25.0%
t 7228
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7228
25.0%
e 7228
25.0%
n 7228
25.0%
t 7228
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7228
25.0%
e 7228
25.0%
n 7228
25.0%
t 7228
25.0%

Property Type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.3 KiB
apartment
7228 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters65052
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapartment
2nd rowapartment
3rd rowapartment
4th rowapartment
5th rowapartment

Common Values

ValueCountFrequency (%)
apartment 7228
100.0%

Length

2025-05-03T11:29:02.333728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T11:29:02.572927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
apartment 7228
100.0%

Most occurring characters

ValueCountFrequency (%)
a 14456
22.2%
t 14456
22.2%
p 7228
11.1%
r 7228
11.1%
m 7228
11.1%
e 7228
11.1%
n 7228
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14456
22.2%
t 14456
22.2%
p 7228
11.1%
r 7228
11.1%
m 7228
11.1%
e 7228
11.1%
n 7228
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14456
22.2%
t 14456
22.2%
p 7228
11.1%
r 7228
11.1%
m 7228
11.1%
e 7228
11.1%
n 7228
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14456
22.2%
t 14456
22.2%
p 7228
11.1%
r 7228
11.1%
m 7228
11.1%
e 7228
11.1%
n 7228
11.1%

Latitude
Real number (ℝ)

Zeros 

Distinct4823
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.032278
Minimum-46.749039
Maximum0
Zeros483
Zeros (%)6.7%
Negative6745
Negative (%)93.3%
Memory size112.9 KiB
2025-05-03T11:29:03.001379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-46.749039
5-th percentile-23.658504
Q1-23.598066
median-23.555869
Q3-23.522756
95-th percentile0
Maximum0
Range46.749039
Interquartile range (IQR)0.07530975

Descriptive statistics

Standard deviation5.9625338
Coefficient of variation (CV)-0.2706272
Kurtosis9.8791332
Mean-22.032278
Median Absolute Deviation (MAD)0.0374628
Skewness3.2567792
Sum-159249.31
Variance35.551809
MonotonicityNot monotonic
2025-05-03T11:29:03.309575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 483
 
6.7%
-23.5053909 37
 
0.5%
-23.604294 35
 
0.5%
-23.522756 21
 
0.3%
-23.5453953 17
 
0.2%
-23.585748 16
 
0.2%
-23.6557135 16
 
0.2%
-23.714221 15
 
0.2%
-23.599178 14
 
0.2%
-23.5510254 14
 
0.2%
Other values (4813) 6560
90.8%
ValueCountFrequency (%)
-46.749039 1
< 0.1%
-46.715115 1
< 0.1%
-46.700223 1
< 0.1%
-46.678478 1
< 0.1%
-46.677847 1
< 0.1%
-46.656944 1
< 0.1%
-46.655399 1
< 0.1%
-46.648904 1
< 0.1%
-46.577355 1
< 0.1%
-46.428927 1
< 0.1%
ValueCountFrequency (%)
0 483
6.7%
-21.8577415 1
 
< 0.1%
-22.3292281 1
 
< 0.1%
-23.3947869 1
 
< 0.1%
-23.41519 2
 
< 0.1%
-23.419688 1
 
< 0.1%
-23.4200734 1
 
< 0.1%
-23.4201966 1
 
< 0.1%
-23.4319192 1
 
< 0.1%
-23.435667 1
 
< 0.1%

Longitude
Real number (ℝ)

Zeros 

Distinct4826
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.500873
Minimum-58.364352
Maximum0
Zeros483
Zeros (%)6.7%
Negative6745
Negative (%)93.3%
Memory size112.9 KiB
2025-05-03T11:29:03.635742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-58.364352
5-th percentile-46.747154
Q1-46.689788
median-46.644793
Q3-46.58511
95-th percentile0
Maximum0
Range58.364352
Interquartile range (IQR)0.104678

Descriptive statistics

Standard deviation11.67507
Coefficient of variation (CV)-0.26838703
Kurtosis9.9074504
Mean-43.500873
Median Absolute Deviation (MAD)0.05009495
Skewness3.4461402
Sum-314424.31
Variance136.30726
MonotonicityNot monotonic
2025-05-03T11:29:04.002186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 483
 
6.7%
-46.6227832 37
 
0.5%
-46.518325 35
 
0.5%
-46.655492 21
 
0.3%
-46.6167926 17
 
0.2%
-46.533696 16
 
0.2%
-46.6363298 16
 
0.2%
-46.533612 15
 
0.2%
-46.704675 15
 
0.2%
-46.6553085 14
 
0.2%
Other values (4816) 6559
90.7%
ValueCountFrequency (%)
-58.3643522 1
 
< 0.1%
-51.9763575 1
 
< 0.1%
-49.108049 1
 
< 0.1%
-49.0607072 12
0.2%
-49.0606445 8
0.1%
-46.955069 1
 
< 0.1%
-46.9412553 1
 
< 0.1%
-46.81286 1
 
< 0.1%
-46.803746 1
 
< 0.1%
-46.803679 1
 
< 0.1%
ValueCountFrequency (%)
0 483
6.7%
-23.51764 1
 
< 0.1%
-23.534683 1
 
< 0.1%
-23.540783 1
 
< 0.1%
-23.545329 1
 
< 0.1%
-23.554196 1
 
< 0.1%
-23.568745 1
 
< 0.1%
-23.569444 1
 
< 0.1%
-23.59818 1
 
< 0.1%
-23.607013 1
 
< 0.1%

Interactions

2025-05-03T11:28:47.506858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:21.826788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:25.067461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:27.902008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:30.642630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:33.429912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:36.617831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:40.333015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:44.663730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:47.734538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:22.138482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:25.435081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:28.186882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:30.920904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:33.733453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:36.962212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:40.673002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:45.099883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:47.937027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:22.430994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:25.710674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:28.416950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:31.230152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:34.048551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:37.615918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:41.010958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:45.315065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:48.182264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:22.990543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:25.972019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:28.671464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:31.570564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:34.500517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:37.877817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:41.414730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:45.584754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:48.430284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:23.315019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:26.225529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:28.919366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:31.801131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:34.815577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:38.151701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:41.981801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:45.975134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:48.902642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:23.583556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:26.522014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:29.244930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:32.112350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:35.215837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:38.926048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:42.838674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:46.462332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:49.276890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:24.011684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:26.985226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:29.653321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:32.383357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:35.544864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:39.176566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:43.214027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:46.738395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:49.485926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:24.269109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:27.369387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:29.910192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:32.754233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:35.966402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:39.595123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:43.762300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:47.019667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:49.739952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:24.592201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:27.648345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:30.198162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:33.115208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:36.333709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:39.917617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:44.123113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-03T11:28:47.255507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-03T11:29:04.276842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
CondoElevatorFurnishedLatitudeLongitudeNewParkingPriceRoomsSizeSuitesSwimming PoolToilets
Condo1.0000.0340.087-0.170-0.2940.0000.6240.6620.4860.6760.4330.1790.469
Elevator0.0341.0000.0940.1200.1210.0370.0880.0170.0000.0240.2080.1500.205
Furnished0.0870.0941.0000.0200.0210.0000.0800.1400.1730.0330.0800.1840.068
Latitude-0.1700.1200.0201.0000.3630.020-0.157-0.202-0.105-0.130-0.2070.023-0.199
Longitude-0.2940.1210.0210.3631.0000.023-0.218-0.319-0.134-0.226-0.1860.133-0.204
New0.0000.0370.0000.0200.0231.0000.0000.0000.0000.0000.0000.0090.000
Parking0.6240.0880.080-0.157-0.2180.0001.0000.6090.5690.6620.6050.3340.610
Price0.6620.0170.140-0.202-0.3190.0000.6091.0000.4040.6460.4870.1560.508
Rooms0.4860.0000.173-0.105-0.1340.0000.5690.4041.0000.7660.4840.2080.505
Size0.6760.0240.033-0.130-0.2260.0000.6620.6460.7661.0000.5340.1520.567
Suites0.4330.2080.080-0.207-0.1860.0000.6050.4870.4840.5341.0000.2760.937
Swimming Pool0.1790.1500.1840.0230.1330.0090.3340.1560.2080.1520.2761.0000.267
Toilets0.4690.2050.068-0.199-0.2040.0000.6100.5080.5050.5670.9370.2671.000

Missing values

2025-05-03T11:28:50.315748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-03T11:28:50.975939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude
09302204722110000Artur Alvim/São Paulorentapartment-23.543138-46.479486
110001484522110000Artur Alvim/São Paulorentapartment-23.550239-46.480718
210001004822110000Artur Alvim/São Paulorentapartment-23.542818-46.485665
310002004822110000Artur Alvim/São Paulorentapartment-23.547171-46.483014
413004105522111000Artur Alvim/São Paulorentapartment-23.525025-46.482436
5117005022110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
610001805212111000Artur Alvim/São Paulorentapartment-23.549840-46.484137
79001504022110000Artur Alvim/São Paulorentapartment-23.539740-46.492670
8100006522110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
91000010022110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude
1120030006105111010110Brooklin/São Paulorentapartment-23.622137-46.694020
1120113000215325834340100Brooklin/São Paulorentapartment-23.619481-46.684028
1120240005894111010110Brooklin/São Paulorentapartment0.0000000.000000
112039000195016834330100Brooklin/São Paulorentapartment-23.624600-46.685282
1120417000220024045450000Brooklin/São Paulorentapartment-23.609608-46.693013
1120537005957312110010Brooklin/São Paulorentapartment-23.617682-46.694963
1120621000300020844331110Brooklin/São Paulorentapartment-23.606891-46.695934
1120738007105511010110Brooklin/São Paulorentapartment0.0000000.000000
112085000235420532121000Brooklin/São Paulorentapartment-23.612287-46.681482
1120915600230016234330010Brooklin/São Paulorentapartment-23.615823-46.685404

Duplicate rows

Most frequently occurring

PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude# duplicates
4212955006932110000Jardim Ângela/São Paulorentapartment-23.604294-46.51832510
1488000140013232110000Iguatemi/São Paulorentapartment-23.585672-46.6812168
72160005021011000Rio Pequeno/São Paulorentapartment-23.565075-46.7506467
2911905557032110010Jardim Ângela/São Paulorentapartment-23.604294-46.5183256
5814003305622110000Sapopemba/São Paulorentapartment-23.585748-46.5336966
2211002704411010000Jabaquara/São Paulorentapartment-23.655714-46.6363305
6515002733111010010Brás/São Paulorentapartment-23.545395-46.6167935
10825004564911010010Casa Verde/São Paulorentapartment0.0000000.0000005
147720019006212120100Iguatemi/São Paulorentapartment-23.583830-46.6835415
3111905557032110010São Lucas/São Paulorentapartment-23.603929-46.5182944